IS RENEWABLE ENERGY PENETRATION PRESERVING FOSSIL FUELS DEPENDENCY?

Transcription

1 IS REEWABLE EERGY PEETRATIO PRESERVIG FOSSIL FUELS DEPEDECY? A EMPIRICAL ASSESSEMET António Cardoso Marques, José Alberto Fuinhas, Diogo André Pereira, Júlio Wilson Fontes amarques@ubi.pt; acardosomarques@gmail.com Barcelona, 7 th February 217

2 Motivation Installed capacity, generation and demand; Visual inspection of facts; What is already known; Research questions; Methodology Data properties; Models ; Fossil fuels Renewables s Choices open to us Barcelona, 7 th February 217

3 Methodology The installed capacity of intermittent renewables reveals a clear increasing tendency: Then why is the capacity factor not growing? The electrification of the economy is on course as well as the diversification of electricity generation: Why does fossil fuel installed capacity remain almost unchanged? The peaks of electricity consumption are still there: How are they being satisfied? So, one must understand the short-run dynamics of adjustments of the several sources and the long-run equilibrium. What are the choices open to us? How should the public policy design evolve? Barcelona, 7 th February 217

4 Installed capacity Methodology The licensing process is mainly based on capacity to install. Effective generation Idle capacity? Capacity factor? Electricity demand Contribution of RES to the electricity mix. Why study them jointly? The use of the three approaches allows a better understanding of the complexity of the relationships between several sources of electricity; and All of the three approaches are criteria for energy policy decisions Barcelona, 7 th February 217

5 Belgium Czech_Republic Denmark Finland 16,, 2,, 4,, 5,, en Methodology 12,, 8,, 4,, 1,, 1,5, 1,, 5, United_ingdom 3,, 2,, 1,, 4,, 3,, 2,, 1,, ,, France Greece Portugal Spain 8,, 6,, 4,, 2,, 6,, 4,, 16,, 12,, 8,, 4,, 4,, 3,, 2,, 1,, 16,, 12,, 8,, 4,, ,, ,, 3,, ,, 1,, Sweden ,, 8,, 6,, 4,, 2,, United_ingdom Idle capacity Capacity effectively used Idle capacity Capacity effectively used Barcelona, 7 th February 217

6 Belgium Czech_Republic Denmark Finland 24,, 2,, 5,, 1, en Methodology 2,, 16,, 12,, 8,, 4,, 1,, 16,, 12,, 8,, 4,, United_ingdom 4,, 3,, 2,, 1,, 8, 6, 4, 2, ,, France Greece Portugal Spain 5,, 4,, 3,, 2,, 1,, 6,, 4,, 2,, 15,, 1,, 5,, 4,, 3,, 2,, 1,, 5,, 4,, 3,, 2,, 1,, ,, , 4, , Sweden United_ingdom 5,, 4,, ,, 2, 1, ,, 1,, Idle capacity Capacity effectively used sol_idle sol_geg Barcelona, 7 th February 217

7 1, 8, 12, 1, 8, 1, 8, 6, 8, 6, 6, Belgium 4, 2, Czech Republic 4, Denmark 4, 2, Finland 4, 2, 12, 1, 8, 16, , France 12, , , Greece 12, 2 221, , Portugal 2 2 6, 8, 8, 12, 6, 12, 6, 6, Methodology 4, 2, 6, 4, 1, 8, 4, 2, 1, 8, 4, 2, 5, 4, 8, 6, 4, 4, 6, , 2, 2, France 8, 2, 6, Greece 6, 3, , 2, 12, 12, 6, , 1, 5, Sweden 8, United ingdom Portugal 2, 4, 8, 6, 3, Spain 1, 2, 15, 4, 2, 6, 4, 2, 2, 1, 1, , Sweden 5, United ingdom 2, 2, 8, , 6, Fossil Fuels 1, 5, , 2, Hydro Power Wind Power Solar PV Electricity Consumption Fossil Fuels Hydro Power Wind Power Marques, Fuinhas, Solar PVPereira, Fontes Electricity Consumption Barcelona, 7 th February 217

8 Methodology The electricity systems reveals several economic inefficiencies: They have to keep or even increase the installed capacity of fossil fuels (mostly on standby) to satisfy the high variability of electricity consumption (e.g. ubik et al., 212, JEPO); There exists an unidirectional causality from RES to fossil fuels (e.g. Al-Mulali et al., 214, RSER; Dogan, 215, RSER; Salim et al., 214, EEECO); The reverse is not mostly proved, i.e., fossil fuels discourage RES (e.g. Al-Mulali et al., 214, RSER; Dogan, 215, RSER; Green and Vasilakos, 21, JEPO; Marques and Fuinhas, 216, RSE); Some literature supports that the deployment of RES does not contribute to reducing the energy imports dependency (e.g. Frondel et al., 21, JEPO); and Public Policies Supporting Renewables and CO 2 emissions are drivers of RES deployment (e.g. Marques et al., 21, JEPO; Aguirre and Ibikunle, 214, JEPO; Polzin et al. 215, JEPO). Barcelona, 7 th February 217

9 Methodology The installed capacity of wind power and of solar PV provokes similar effects on the on-renewable Energy Sources (RES)?; Is there a substitution effect between Hydropower, Intermittent renewables (RES-I) and fossil sources electricity generation?; and How is the system handling with the demand peaks, both on the short-run dynamics and on the long-run equilibrium? Barcelona, 7 th February 217

10 Motivation Countries under analysis: Belgium, Czech Republic, Denmark, Finland, France, Greece, Portugal, Spain, Sweden and United ingdom. Models and explained variables: FOSSIL: electricity generation from fossil fuels; COAL: idem, from coal; OIL: idem, from oil; GAS: idem, from natural gas; HYDRO: idem, from hydro power; RES-I: idem, from intermittent renewables. Explanatory variables: Installed capacity of wind power; Installed capacity of solar PV; Intermittent (RES-I) share; Hydro power share; Coverage rate of electricity imports by exports; Electricity consumption intensity; High electricity consumption peaks during the year; Carbon dioxide emissions; Total energy real price index; Accumulated number of policy-driven instruments; Accumulated number of market-driven instruments. Barcelona, 7 th February 217

11 Motivation Cross-section dependence and unit roots tests CD-test (Pesaran, 24); 2 nd generation CIPS unit root test (Pesaran, 27) Correlation matrix and variance inflation factors Autoregressive Distributed Lag (ARDL) model Breakdown of the total effects into both short-run (semi-elasticities) and long-run (elasticities) effects Model Specification tests Heteroskedasticity, serial correlation, contemporaneous correlation; and Fixed Effects vs. Random Effects Models estimation and elasticities computation Barcelona, 7 th February 217

12 Motivation Table 1. Cross-section dependence and unit roots test Cross section dependence Unit roots test (CIPS) Level First differences CD-test Corr Abs(corr) o trend With trend o trend With trend LFOSSIL_EG 16.4*** *** * -9.8*** *** LCOAL_EG 11.29*** ** * *** *** LOIL_EG 19.39*** *** *** *** LGAS_EG 18.42*** *** *** LHYDRO_EG *** -5.58*** *** *** LRESI_EG 3.12*** *** -5.46*** LWID_IC 24.71*** *** *** LSOL_IC 24.17*** *** *** LFOSSIL_IC 3.68*** *** *** LHYDRO_SH *** *** *** *** LRESH_SH 3.44*** *** *** LCOAL_EH 2.74*** *** -2.2** *** *** LOIL_SH 24.73*** *** *** -8.4*** LGAS_SH 23.47*** *** *** LRXM *** ** *** *** LPEA 18.87*** *** *** *** *** LCOS 3.61*** *** *** LRXM_FOSSIL 1.96** ** *** *** *** LCO *** ** *** *** LPRICE 28.73*** *** -6.88*** LPOLI_DRI 29.22*** *** -8.61*** LMAR_DRI 28.49*** * *** *** otes: *, **, ***, denote statistical significance at 1%, 5% and 1% level, respectively. CD-test has (,1) distribution under H: cross-section independence; panel unit roots test (CIPS) tests the H: series are I(1) ey points: The CD-test strongly supports the presence of cross-section in all variables, except LRXM, LHYDRO_EG, LHYDRO_SH. Second generation CIPS unit roots test proves that all variables are I() on their first differences, but several variables are borderline I(1). They are not I(2)! Barcelona, 7 th February 217

13 Motivation Equations (1) to (6) represents the general UECM (unrestricted error correction mechanism) form of the ARDL model, in order to decompose the dynamic relationship of variables in the short and long-run. DLFOSSIL_EG i,t = α 1i + α 2i TRED + DLCOAL_EG i,t = β 1i + m=1 p= DLOIL_EG i,t = γ 1i + γ 2i TRED + DLGAS i,t = ε 1i + m=1 p= m=1 p= θ 2imp DLX 2imt p + m=1 p= ψ 2imp DLX 4imt p + DLRESI_EG i,t = ζ 1i + ζ 2i TRED + DLHYDRO_EG i,t = θ 1i + m=1 p= m=1 p= λ 2imp DLX 1imt p + p=1 π 2imp DLX 3imt p + p=1 φ 2imp DLX 6imt p + p=1 θ 1im LCOAL_EG it 1 + p=1 ψ 1im LGAS it 1 + φ 2imp DLX 5imt p + p=1 p=1 λ 1im LFOSSIL_EG it 1 + m=1 p=o π 1im LOIL_EG it 1 + m=1 p=o m=1 p=o λ 3imp LY 1imt p + μ 1it (1) θ 3imp LY 2imt p + μ 2it (2) m=1 p=o π 3imp LY 3imt p + μ 3it (3) ψ 3imp LX 4imt p + μ 4it (4) φ 1im LRESI_EG it 1 + φ 1im LHYDRO_EG it 1 + m=1 p=o m=1 p=o φ 3imp LY 5imt p + μ 5it (5) φ 3imp LY 6imt p + μ 6it (6) Barcelona, 7 th February 217

14 Motivation Table 2. Specification tests Models FOSSIL COAL OIL GAS HYDRO RES-I Modified Wald test 375.3*** 49.61*** 29.46*** *** 11.7*** *** Wooldridge test *** *** *** *** 32.3*** *** Pesaran test ** *** Frees test.495**.275*** **.337** Friedman test *** Hausman RE vs. FE 59.74*** 39.93*** 37.75*** 64.7*** *** 48.51*** otes: ***, ** denote statistical significance at 1% and 5% level, respectively; the modified Wald test has χ2 distribution and tests H: σ_c^2=σ^2, for c=1,,; the Wooldridge test is normally distributed (,1), and tests H: no serial correlation; Pesaran, Frees and Friedman test the H: residuals are not correlated; Hausman results for H: difference in coefficient is not systematic including the constant. ey points: The specification tests indicate the presence of: (i) heteroskedasticity in all models; (ii) panel first order correlation in all models; (iii) contemporaneous correlation in all but model GAS; (iv) the existence of FE. Barcelona, 7 th February 217

15 Motivation Cross-section dependence Strongly supported the presence of cross-section in all variables Unit roots Mostly of variables are I(1) in their levels, but some variables are borderline I() ARDL modelling Adequacy of assessing the short-run dynamics and the long-run equilibrium. Allows variables with long memory patterns to be handled appropriately, and with borderline variables. Specification tests Heteroskedasticity, first order autocorrelation, contemporaneous correlation, and panel fixed effects ARDL approach estimated by Driscoll-raay estimator with fixed effects Driscoll raay (1998) estimator is a covariance matrix estimator, and their small-sample properties are considerably better than the alternative covariance estimators, mainly when cross-sectional dependence, heteroskedasticity, autocorrelation, and contemporaneous correlation are present (Hoechle, 27, SJ) Barcelona, 7 th February 217

16 Motivation Methodology Table 4. Elasticities, semi-elasticities, and adjustment speeds of fossil fuel models Models FOSSIL COAL OIL GAS Short-run (semi-elasticities) DLSOL_IC -.562*** -.546* DLHYDRO_SH -.323*** *** ** *** DLRESH_SH *** *** DLRXM -.529*** -.718*** -.873*** DLPEA.1114***.148***.1436***.2332*** DLCOS *** *** 2.642*** *** Speed of adjustment ECM -.348*** -.273*** *** *** Long-run (elasticities) LWID_IC.1822***.2581***.2234*** LSOL_IC -.479*** -.173*** -.217*** LHYDRO_SH *** LRESH_SH ***.33*** LRXM *** -.237*** *** *** LPEA.4968*** LCOS 1.547*** *** *** *** ey points: Installed capacity of wind power has increased fossil fuels electricity generation. The electricity generation from natural gas has been called to fulfil the consumption peaks. The expected substitution effect is absent with wind power. It is effective with hydropower and solar PV. Trend -.2*** -.428*** Const *** *** *** 9.259** OBS R otes: *, **, *** denote statistical significance at 1%, 5% and 1% level, respectively. In sum, peaks on demand and wind power capacity are preserving fossil fuels, in the long-run. Barcelona, 7 th February 217

17 Motivation Methodology ey points: Hydropower has been crucial to accommodate the RES-I The complementary effect between natural gas and RES-I is confirmed CO2 emissions and marketdriven instruments have been relevant drivers of RES-I. However, the peaks on demand have been an effective barrier to RES-I. Table 4. Elasticities, semi-elasticities, and adjustment speeds of RES-I and HYDRO models Model RES-I Model HYDRO Short-run (semi-elasticities) Short-run (semi-elasticities) DLFOSSIL_IC *** DLWID_IC -.75** DLCOAL_SH *** DLCOAL_SH *** DLGAS_SH.2573*** DLGAS_SH * DLPEA.1221*** DLRESH_SH -.261*** DLPRICE.9657*** DLCOS 1.372** LPOLI_DRI * Speed of adjustment Speed of adjustment ECM -.142*** ECM -.819*** Long-run (elasticities) Long-run (elasticities) LFOSSIL_IC *** LWID_IC.391*** LCOAL_SH *** LSOL_IC -.149* LGAS_SH.9624*** LCOAL_SH ** LPEA ** LOIL_SH.1436*** LRXM_FOSSIL 1.288*** LGAS_SH ** LCO2 8.15*** LRXM -.847*** LPRICE *** LCOS.424** LPOLI_DRI ** LMAR_DRI *** Trend.231*** Const *** Const *** OBS R R otes: *, **, *** denote statistical significance at 1%, 5% and 1% level, respectively. Barcelona, 7 th February 217

18 Motivation Methodology The two new renewables are not similar: the substitution effect has been effective with solar PV, in contrary to wind power; The variability of wind power has put pressure on domestic electricity generation systems; atural gas plays a crucial role in backing the intermitten renewables in the long-run; Hydropower and its availability of energy storage makes it a natural support for wind power, but it has been not sufficient, which implies the backup from fossil fuels; Peaks in demand are constraints to the development of renewables. They have to be met by fossil fuels; and Demand-side management policies are needed to reshape the electricity demand in order to achieve electrification from renewables rather than from fossil fuels. Barcelona, 7 th February 217

19 Motivation Methodology s Integrating renewables is not only about building new wind farms or solar PV power plants; Responding to the fluctuation of the instantaneous availability of electricity needs to be met; The economy ought to be prepared for: Promoting load shifting from peak periods towards off-peak or valley periods; Storing electricity under different forms, such as in thermal stores; Increasing the use of e-vehicles, making effective the V2G and G2V strategies; Improving electric mobility, by providing an electric charger grid as well as domestic chargers at a reasonable cost; Rewarding change of consumption routines, for instance through electricity tariffs; and Promoting further self-generation in both homes and businesses. Once these challenges are met, then we will be closer to achieving the most desirable diversification of the electricity mix. Barcelona, 7 th February 217

20 IS REEWABLE EERGY PEETRATIO PRESERVIG FOSSIL FUELS DEPEDECY? A EMPIRICAL ASSESSEMET António Cardoso Marques, José Alberto Fuinhas, Diogo André Pereira, Júlio Wilson Fontes amarques@ubi.pt; acardosomarques@gmail.com Barcelona, 7 th February 217